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To plan movements toward objects our brain must recognize whether retinal displacement is due to self-motion and/or to object-motion. Here, we aimed to test whether motion areas are able to segregate these types of motion. We combined an event-related functional magnetic resonance imaging experiment, brain mapping techniques, and wide-field stimulation to study the responsivity of motion-sensitive areas to pure and combined self- and object-motion conditions during virtual movies of a train running within a realistic landscape. We observed a selective response in MT to the pure object-motion condition, and in medial (PEc, pCi, CSv, and CMA) and lateral (PIC and LOR) areas to the pure self-motion condition. Some other regions (like V6) responded more to complex visual stimulation where both object- and self-motion were present. Among all, we found that some motion regions (V3A, LOR, MT, V6, and IPSmot) could extract object-motion information from the overall motion, recognizing the real movement of the train even when the images remain still (on the screen), or moved, because of self-movements. We propose that these motion areas might be good candidates for the "flow parsing mechanism," that is the capability to extract object-motion information from retinal motion signals by subtracting out the optic flow components., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally <10(-1)Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 10(0)-10(2)Hz. We examined phase-shifted interactions in the delta (2-3.5 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Spontaneous fMRI fluctuations are organized in large-scale spatiotemporal structures, or resting-state networks (RSN). However, it is unknown how task performance affects RSN dynamics. We use MEG to measure slow (∼0.1 Hz) coherent fluctuations of band-limited power (BLP), a robust correlate of RSN, during rest and movie observation and compare them to fMRI-RSN. BLP correlation, especially in α band, dropped in multiple RSN during movie although overall topography was maintained. Variability of power correlation increased in visual occipital cortex, and transient decrements corresponded to scenes perceived as "event boundaries." Additionally, stronger task-dependent interactions developed between vision and language networks in θ and β bands, and default and language networks in γ band. The topography of fMRI connectivity and relative changes induced by the movie were well matched to MEG. We conclude that resting-state and task network interactions are clearly different in the frequency domain despite maintenance of underlying network topography., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The principles of functional specialization and integration in the resting brain are implemented in a complex system of specialized networks that share some degree of interaction. Recent studies have identified wider functional modules compared to previously defined networks and reported a small-world architecture of brain activity in which central nodes balance the pressure to evolve segregated pathways with the integration of local systems. The accurate identification of such central nodes is crucial but might be challenging for several reasons, e.g. inter-subject variability and physiological/pathological network plasticity, and recent works reported partially inconsistent results concerning the properties of these cortical hubs. Here, we applied a whole-brain data-driven approach to extract cortical functional cores and examined their connectivity from a resting state fMRI experiment on healthy subjects. Two main statistically significant cores, centered on the posterior cingulate cortex and the supplementary motor area, were extracted and their functional connectivity maps, thresholded at three statistical levels, revealed the presence of two complex systems. One system is consistent with the default mode network (DMN) and gradually connects to visual regions, the other centered on motor regions and gradually connects to more sensory-specific portions of cortex. These two large scale networks eventually converged to regions belonging to the medial aspect of the DMN, potentially allowing inter-network interactions., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Purpose: To investigate white matter heterogeneity using a multichannel segmentation of a large sample of structural and diffusion magnetic resonance imaging (MRI) data., Materials and Methods: A sample of 50 subjects was segmented using channels comprising exclusively structural (longitudinal and transverse relaxation times T1 and T2 and transverse relaxation rate R2*) and diffusion-based MRI indices (mean diffusivity and fractional anisotropy). These data were analyzed using a data driven approach in which no prior information was used., Results: The analysis revealed the splitting of white matter into two subclasses in which the longitudinal fasciculi were distinguished from inferior/superior ones. The distribution of the adopted indices in the obtained clusters showed that R2* was mainly responsible for this splitting., Conclusion: This result supports the observation, previously hypothesized in the literature, that R2* is influenced by the fiber orientation., (Copyright © 2012 Wiley Periodicals, Inc.)

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering., (Copyright © 2012 Elsevier Inc. All rights reserved.)

We recently suggested that serotonin 7 (5-Ht7) receptors may play a role in ADHD-like symptoms, at least in animal models. A mixed 5-Ht(1a/7) agonist, 8-OH-DPAT, counteracted the augmented levels of basal impulsivity, observed after treatment with a selective 5-Ht7 antagonist, SB269970 (Leo et al., 2009). In the present study, these serotonergic compounds were investigated by pharmacological magnetic resonance imaging (phMRI) at 4.7 T in adult isoflurane-anaesthetized rats. Axial echo-planar images were collected from the prefrontal cortex (PFC), ventral (nucleus accumbens, NAcc) and dorsal (dStr) striata, the hippocampus and the thalamus. After consecutive image collection for 30 min (50 baseline images), adult rats received either SB269970 (3mg/kg), 8-OH-DPAT (0.06 mg/kg) or saline intra-peritoneally (i.p.) via a remote cannula; the images were then collected for further 30 min (50 post-treatment images). Data were analysed 1) through an activation map generated on brain templates, obtained by using animals from each experimental group; 2) by a two-way ANOVA for the evaluation of temporal profiles, extracted within selected ROIs of each animal. Both compounds increased the BOLD signal in the areas of interest: SB269970, the selective 5-Ht7 antagonist, induced a significant effect in the PFC, particularly the orbital (oPFC) region, and in the NAcc. This effect started 6 to 12 min after drug administration, reached a maximum (+2.8%/+2.3%) at 12 to 18 min, and then moved to the dorsal thalamic nuclei. In contrast, the effects of 8-OH-DPAT were first observed in midline thalamic nuclei, and later appeared in forebrain regions: its effects were modest and transient within the NAcc and oPFC (+1.7% at 18 to 24 min after injection), whereas they were higher and long-lasting in the dStr and PFC, specifically the medial (mPFC) region (+3.1%/+4.0% from 15 min after drug administration onwards). The brain BOLD changes, reported as a consequence of selective 5-Ht7 antagonist administration, seemed restricted to the oPFC, NAcc and dorso-thalamic circuits, whereas the non-selective blockade of serotonergic receptors affected the mPFC, dStr and mid(line)-thalamic circuitry. The present findings revealed two differential serotonergic sub-pathways, as evidenced by the detection of physiological vascular feedback and/or neuronal activation., (Copyright © 2011 Elsevier Inc. All rights reserved.)

To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level). Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach. (3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination after source localization, as implemented in the proposed pipeline, provides a lower source-level signal distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed by an improved specificity in the retrieval of RSNs from experimental data.

Rationale: Adolescent rodents differ markedly from adults in several neuro-behavioural parameters. Moreover, 'paradoxical' responses to psychostimulants have been reported at this age., Objectives: Thus, we investigated the responses of adolescent (post-natal day, PND, 34 to 43) and adult (PND >60) Sprague-Dawley male rats to the psychostimulant drug methylphenidate (MPH). We used pharmacological magnetic resonance imaging (phMRI) performed at 4.7 T under isoflurane anaesthesia. Following anatomical MRI, axial gradient echo images were collected continuously. After baseline recording (32 min), animals received MPH (0 or 4 mg/kg i.p.) and were recorded for further 32 min., Results: Region-specific changes in the blood-oxygenation level dependent (BOLD) signal were evident as a function of age. As expected, among adults MPH induced an increase of BOLD signal in nucleus accumbens (NAcc) and prefrontal cortex (PFC), with no effects in the hippocampus (Hip). Notably, among adolescents, MPH induced a marked and generalised decrease of BOLD signal, which occurred earlier in NAcc and PFC whilst being delayed in the Hip. Any bias in BOLD responses was excluded by the measurement of physiological parameters., Conclusions: The present findings highlight the utility of phMRI in animal models. The peculiar negative BOLD effect found in adolescent rats may be suggestive of a reduced cerebro-vascular feedback and/or an increased MPH-induced neuronal activation. Data are relevant for a better understanding of brain/behavioural regulation during adolescent development. Moreover, a greater understanding of the differences between adult and adolescent drug responses will aid in the development of a more appropriate age-specific treatment strategy.

In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is proposed. The Bayesian framework is appealing since complex models can be adopted in the analysis both for the image and noise model. Here, the noise autocorrelation is taken into account by adopting an AutoRegressive model of order one and a versatile non-linear model is assumed for the task-related activation. Model parameters include the noise variance and autocorrelation, activation amplitudes and the hemodynamic response function parameters. These are estimated at each voxel from samples of the Posterior Distribution. Prior information is included by means of a 4D spatio-temporal model for the interaction between neighbouring voxels in space and time. The results show that this model can provide smooth estimates from low SNR data while important spatial structures in the data can be preserved. A simulation study is presented in which the accuracy and bias of the estimates are addressed. Furthermore, some results on convergence diagnostic of the adopted algorithm are presented. To validate the proposed approach a comparison of the results with those from a standard GLM analysis, spatial filtering techniques and a Variational Bayes approach is provided. This comparison shows that our approach outperforms the classical analysis and is consistent with other Bayesian techniques. This is investigated further by means of the Bayes Factors and the analysis of the residuals. The proposed approach applied to Blocked Design and Event Related datasets produced reliable maps of activation.

We propose an application of a Bayesian methodology to dynamic MR images of protons J-coupled to 13C nuclei for monitoring the in vivo 13C-glucose uptake of mouse brain. The very low population of these protons and the random noise make the analysis of these images extremely difficult. The proposed method restores the images and provides an "activation" map of the mouse brain by means of a hypothesis testing procedure. The restoration step is performed in the Bayesian framework so that among the other advantages of a stochastic approach, it is possible to model spatial and temporal information about neighboring pixels. This leads to a restoration procedure able to reduce the noise level while preserving the information about the edges of signal areas. Based on the restored images, the testing procedure provides us with a reliable map of pixels characterized by the 13C-glucose uptake.

Abstract: The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community. [Copyright &y& Elsevier]